add scripts

script/eval.sh

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+python train_dist.py --skip_nll 1 --eval_generation --pretrained  $1 ddpm.model_var_type "fixedlarge" data.batch_size_test 3 ddpm.ema 1 num_val_samples 3 

script/train_prior.sh

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+loss="mse_sum"
+NGPU=$1 ## 1 #8
+num_node=2
+mem=32
+BS=10 
+lr=2e-4
+ENT="python train_dist.py --num_process_per_node $NGPU "
+train_vae=False
+cmt="lion"
+ckpt="./lion_ckpt/unconditional/car/checkpoints/vae_only.pt"
+
+$ENT \
+    --config "./lion_ckpt/unconditional/car/cfg.yml" \
+    latent_pts.pvd_mse_loss 1 \
+    vis_latent_point 1 \
+    num_val_samples 24 \
+    ddpm.ema 1 \
+    ddpm.use_bn False ddpm.use_gn True \
+    ddpm.time_dim 64 \
+    ddpm.beta_T 0.02 \
+    sde.vae_checkpoint $ckpt \
+    sde.learning_rate_dae $lr sde.learning_rate_min_dae $lr \
+    trainer.epochs 18000 \
+    sde.num_channels_dae 2048 \
+    sde.dropout 0.3 \
+    latent_pts.style_prior 'models.score_sde.resnet.PriorSEDrop' \
+    sde.prior_model 'models.latent_points_ada_localprior.PVCNN2Prior' \
+    sde.train_vae $train_vae \
+    sde.embedding_scale 1.0 \
+    viz.save_freq 1000 \
+    viz.viz_freq -200 viz.log_freq -1 viz.val_freq -10000 \
+    data.batch_size $BS \
+    trainer.type 'trainers.train_2prior' \
+    cmt $cmt 

script/train_vae.sh

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+DATA=" ddpm.input_dim 3 data.cates car "
+NGPU=$1 # 
+num_node=1
+mem=40 
+BS=32 
+
+ENT="python train_dist.py --num_process_per_node $NGPU "
+kl=0.5  
+lr=1e-3
+latent=1
+skip_weight=0.01 
+sigma_offset=6.0
+loss='l1_sum'
+
+$ENT ddpm.num_steps 1 ddpm.ema 0 \
+    trainer.opt.vae_lr_warmup_epochs 0 \
+    latent_pts.ada_mlp_init_scale 0.1 \
+    sde.kl_const_coeff_vada 1e-7 \
+    trainer.anneal_kl 1 sde.kl_max_coeff_vada $kl \
+    sde.kl_anneal_portion_vada 0.5 \
+    shapelatent.log_sigma_offset $sigma_offset latent_pts.skip_weight $skip_weight \
+    trainer.opt.beta2 0.99 \
+    data.num_workers 4 \
+    ddpm.loss_weight_emd 1.0 \
+    trainer.epochs 8000 data.random_subsample 1 \
+    viz.viz_freq -400 viz.log_freq -1 viz.val_freq 200 \
+    data.batch_size $BS viz.save_freq 2000 \
+    trainer.type 'trainers.hvae_trainer' \
+    model_config default shapelatent.model 'models.vae_adain' \
+    shapelatent.decoder_type 'models.latent_points_ada.LatentPointDecPVC' \
+    shapelatent.encoder_type 'models.latent_points_ada.PointTransPVC' \
+    latent_pts.style_encoder 'models.shapelatent_modules.PointNetPlusEncoder' \
+    shapelatent.prior_type normal \
+    shapelatent.latent_dim $latent trainer.opt.lr $lr \
+    shapelatent.kl_weight ${kl} \
+    shapelatent.decoder_num_points 2048 \
+    data.tr_max_sample_points 2048 data.te_max_sample_points 2048 \
+    ddpm.loss_type $loss cmt "lion" \
+    $DATA viz.viz_order [2,0,1] data.recenter_per_shape False data.normalize_global True